
******************************************
***** Study 1 - Uscinski et al. 2022 *****
*********** Analyses do file *************
******************************************


* Changing directory
**<<<ADD DIRECTORY>>>

* Loading the dataset data (.dta)
use "Study 1 data.dta", replace

* Respodents ID
gen id=_n


* Gender
gen gender=1 if female==1
replace gender=0 if female==0
label define gender_lab 0 "Male" 1 "Female"
label values gender gender_lab
tab gender female
label var gender "Gender (female=1)"
fre gender

* Age group
recode age (18/24=1)(25/34=2)(35/44=3)(45/54=4)(55/64=5)(65/100=6), gen(age_group)
label define age_g_lab 1 "18-24" 2 "25-34" 3 "35-44" 4 "45-54" 5 "55-64" 6 "65+"
label value age_group age_g_lab
tab age age_group
label var age_group "Age groups"
fre age_group

* White respondent
rename white white2
gen white=1 if white2==1
replace white=0 if white2==0
label define white_lab 0 "Non-White" 1 "White"
label values white white_lab
tab white2 white
label var white "White respondent"
fre white

* College education
recode edu (1/2=0)(3/6=1), gen(college_educ)
label define coll_lab 0 "No college education" 1 "College education"
label values college_educ coll_lab
tab edu college_educ
label var college_educ "College education"
fre college_educ

* Income
label var income "Income (7 categories)"
fre income

* Republican respondent
rename rep rep2
gen rep=1 if rep2==1
replace rep=0 if rep2==0
label define rep_lab 0 "Democrat" 1 "Republican"
label values rep rep_lab
tab rep2 rep, miss
label var rep "Republican respondent"
fre rep

* Ideology
tab ideo
recode ideo (1/3=0)(4=1)(5/7=2), gen(ideo_group)
label define ideo_lab 0 "Liberal" 1 "Moderate" 2 "Conservative"
label values ideo_group ideo_lab
tab ideo ideo_group
fre ideo_group

* The Conspiracy thinking scale
rename conthink CT_scale=
replace CT_scale=(CT_scale-1)/4
label var CT_scale "The Conspiracy thinking scale"
sum CT_scale


** The "partisan" conspiracy theories **
* 1st "partisan" conspiracy theory - Republicans stole elections
tab belief_repsteal
gen CTI_Rep_steal_elec=belief_repsteal
label define ct_yesno 0 "Disagree/neutral/No/DK" 1 "Agree/Yes"
label values CTI_Rep_steal_elec ct_yesno
tab CTI_Rep_steal_elec belief_repsteal

* 2nd "partisan" conspiracy theory - Elite trafficking
tab belief_hollywood
gen CTI_elite_trafficking=belief_hollywood
label values CTI_elite_trafficking ct_yesno
tab CTI_elite_trafficking belief_hollywood

* 3nd "partisan" conspiracy theory - Reagan/Iran Hostage deal
tab belief_hostage
gen CTI_iran_hostage=belief_hostage
label values CTI_iran_hostage ct_yesno
tab CTI_iran_hostage belief_hostage

* 4th "partisan" conspiracy theory - George Soros
tab belief_soros
gen CTI_soros=belief_soros
label values CTI_soros ct_yesno
tab CTI_soros belief_soros

* 5th "partisan" conspiracy theory - Global warming is a hoax
tab belief_globalwarming
gen CTI_global_warming=belief_globalwarming
label values CTI_global_warming ct_yesno
tab CTI_global_warming belief_globalwarming

* 6th "partisan" conspiracy theory - Obama faked his citizenship ("Birther")
tab belief_birther
gen CTI_Obama_faked_birth=belief_birther
label values CTI_Obama_faked_birth ct_yesno
tab CTI_Obama_faked_birth belief_birther

* 7th "partisan" conspiracy theory - Clinton nukes to Russians
tab belief_clintonnuke
gen CTI_Clinton_nukes=belief_clintonnuke
label values CTI_Clinton_nukes ct_yesno
tab CTI_Clinton_nukes belief_clintonnuke

* 8th "partisan" conspiracy theory - COVID-19 exaggerated
tab belief_cexaggerate
gen CTI_COVID_exaggerated=belief_cexaggerate
label values CTI_COVID_exaggerated ct_yesno
tab CTI_COVID_exaggerated belief_cexaggerate

* 9th "partisan" conspiracy theory - COVID-19 spread/released on purpose
tab belief_cpurpose
gen CTI_COVID_spread_purpose=belief_cpurpose
label values CTI_COVID_spread_purpose ct_yesno
tab CTI_COVID_spread_purpose belief_cpurpose

* 10th "partisan" conspiracy theory - COVID-19 vaccine dangerous 
tab belief_vaccine
gen CTI_COVID_vaccine=belief_vaccine
label values CTI_COVID_vaccine ct_yesno
tab CTI_COVID_vaccine belief_vaccine

* 11th "partisan" conspiracy theory - There's a "deep state" in government  
tab belief_deepstate
gen CTI_deep_state=belief_deepstate
label values CTI_deep_state ct_yesno
tab CTI_deep_state belief_deepstate

* 12th "partisan" conspiracy theory - The dangers of vaccines are hidden  
tab belief_vaccine2
gen CTI_vaccine_dang_hidden=belief_vaccine2
label values CTI_vaccine_dang_hidden ct_yesno
tab CTI_vaccine_dang_hidden belief_vaccine2


** The "non-partisan" conspiracy theories **

* 1st "non-partisan" conspiracy theory - info. on UFOs hidden by Gov.
tab belief_ufo
gen CTI_UFOs_gov_hides=belief_ufo
label values CTI_UFOs_gov_hides ct_yesno
tab CTI_UFOs_gov_hides belief_ufo

* 2nd "non-partisan" conspiracy theory - COVID-19 used to install tracking devices
tab belief_tracking
gen CTI_COVID_tracking=belief_tracking
label values CTI_COVID_tracking ct_yesno
tab CTI_COVID_tracking belief_tracking

* 3rd "non-partisan" conspiracy theory - Moon landing faked
tab belief_moon
gen CTI_moon_landing_fake=belief_moon
label values CTI_moon_landing_fake ct_yesno
tab CTI_moon_landing_fake belief_moon

* 4th "non-partisan" conspiracy theory - O.J. Simpson framed by police
tab belief_simpson
gen CTI_Simpson_framed=belief_simpson
label values CTI_Simpson_framed ct_yesno
tab CTI_Simpson_framed belief_simpson

* 5th "non-partisan" conspiracy theory - The 1% of richest control the Gov.
tab belief_onepercent
gen CTI_one_percent=belief_onepercent
label values CTI_one_percent ct_yesno
tab CTI_one_percent belief_onepercent

* 6th "non-partisan" conspiracy theory - believer in QANON
tab belief_believer
gen CTI_QAnon_believer=belief_believer
label values CTI_QAnon_believer ct_yesno
tab CTI_QAnon_believer belief_believer

* 7th "non-partisan" conspiracy theory - the assassination of Sen. Robert (Bobby) Kennedy 
tab belief_bobby
gen CTI_RFK_assassin=belief_bobby
label values CTI_RFK_assassin ct_yesno
tab CTI_RFK_assassin belief_bobby

* 8th "non-partisan" conspiracy theory - the Rothschilds controls Gov., war, etc.
tab belief_rothschilds
gen CTI_Rothschilds_control=belief_rothschilds
label values CTI_Rothschilds_control ct_yesno
tab CTI_Rothschilds_control belief_rothschilds

* 9th "non-partisan" conspiracy theory - truth about Sandy Hook school shooting hidden
drop belief_sandyhook
recode q63 (1=1)(2/4=0), gen(belief_sandyhook)
tab q63 belief_sandyhook
gen CTI_Sandy_Hook=belief_sandyhook
label values CTI_Sandy_Hook ct_yesno
tab CTI_Sandy_Hook belief_sandyhook

* 10th "non-partisan" conspiracy theory - Gov. assassination of entertainers 
tab belief_govassass
gen CTI_Gov_assassin=belief_govassass
label values CTI_Gov_assassin ct_yesno
tab CTI_Gov_assassin belief_govassass

* 11th "non-partisan" conspiracy theory - The assassination of JFK 
tab belief_jfk
gen CTI_JFK_assassin=belief_jfk
label values CTI_JFK_assassin ct_yesno
tab CTI_JFK_assassin belief_jfk

* 12th "non-partisan" conspiracy theory - a national conspiracy to kill police 
tab belief_killpolice
gen CTI_kill_police=belief_killpolice
label values CTI_kill_police ct_yesno
tab CTI_kill_police belief_killpolice

* 13th "non-partisan" conspiracy theory - fluorescent lightbulbs make people more obedient 
tab belief_lightbulbs
gen CTI_lightbulbs=belief_lightbulbs
label values CTI_lightbulbs ct_yesno
tab CTI_lightbulbs belief_lightbulbs

* 14th "non-partisan" conspiracy theory - mind-controlling technology in TV broadcast 
tab belief_mindcontrol
gen CTI_mind_control_TV=belief_mindcontrol
label values CTI_mind_control_TV ct_yesno
tab CTI_mind_control_TV belief_mindcontrol

* 15th "non-partisan" conspiracy theory - the assassination of Martin Luther King (MLK) 
tab belief_mlk
gen CTI_MLK_assassin=belief_mlk
label values CTI_MLK_assassin ct_yesno
tab CTI_MLK_assassin belief_mlk

* 16th "non-partisan" conspiracy theory - Epstein was murdered as part of a cover-up  
tab belief_epstein
gen CTI_epstein=belief_epstein
label values CTI_epstein ct_yesno
tab CTI_epstein belief_epstein

* 17th "non-partisan" conspiracy theory - FDA preventing public from getting natural cures for cancer 
tab belief_fda
gen CTI_FDA_cancer=belief_fda
label values CTI_FDA_cancer ct_yesno
tab CTI_FDA_cancer belief_fda

* 18th "non-partisan" conspiracy theory - FDR knew about Pearl Harbor?
tab belief_fdrlied
gen CTI_FDR_lied=belief_fdrlied
label values CTI_FDR_lied ct_yesno
tab CTI_FDR_lied belief_fdrlied

* 19th "non-partisan" conspiracy theory - Fluoride in water supply for sinister reasons
tab belief_fluoride
gen CTI_Fluoride_sinister=belief_fluoride
label values CTI_Fluoride_sinister ct_yesno
tab CTI_Fluoride_sinister belief_fluoride

* 20th "non-partisan" conspiracy theory - dangers of GMO foods hidden 
tab belief_gmos
gen CTI_GMOs_dangers=belief_gmos
label values CTI_GMOs_dangers ct_yesno
tab CTI_GMOs_dangers belief_gmos

* 21th "non-partisan" conspiracy theory - phones cause cancer  
tab belief_cellphone2
gen CTI_phones_cancer=belief_cellphone2
label values CTI_phones_cancer ct_yesno
tab CTI_phones_cancer belief_cellphone2

* 22th "non-partisan" conspiracy theory - 5G technology spread COVID  
tab belief_cellphone
gen CTI_5G_COVID=belief_cellphone
label values CTI_5G_COVID ct_yesno
tab CTI_5G_COVID belief_cellphone

* 23th "non-partisan" conspiracy theory - 9/11 an 'inside job' (truther) 
tab belief_truther
gen CTI_Sept11_truther=belief_truther
label values CTI_Sept11_truther ct_yesno
tab CTI_Sept11_truther belief_truther

* 24th "non-partisan" conspiracy theory - AIDS harms minorities
tab belief_aids1
gen CTI_AIDS_minorities=belief_aids1
label values CTI_AIDS_minorities ct_yesno
tab CTI_AIDS_minorities belief_aids1

* 25th "non-partisan" conspiracy theory - Big Pharma "invents" new diseases
tab belief_pharma
gen CTI_big_pharma=belief_pharma
label values CTI_big_pharma ct_yesno
tab CTI_big_pharma belief_pharma

* 26th "non-partisan" conspiracy theory - Bill Gates is behind COVID-19
tab belief_billgates
gen CTI_Bill_Gates=belief_billgates
label values CTI_Bill_Gates ct_yesno
tab CTI_Bill_Gates belief_billgates

* 27th "non-partisan" conspiracy theory - Osama bin Laden still alive
tab belief_binladen
gen CTI_bin_Laden=belief_binladen
label values CTI_bin_Laden ct_yesno
tab CTI_bin_Laden belief_binladen



** RESHAPE of the data - from Wide to Long
* Conspiracy theory number
*reshape long CON_, i(id) j(CTI_num) string
reshape long CTI_, i(id) j(CTI_string) string
order CTI_string, after(CTI_)
fre CTI_string
gen CTI_num=1 if CTI_string=="Rep_steal_elec"
replace CTI_num=2 if CTI_string=="elite_trafficking"
replace CTI_num=3 if CTI_string=="iran_hostage"
replace CTI_num=4 if CTI_string=="soros"
replace CTI_num=5 if CTI_string=="global_warming"
replace CTI_num=6 if CTI_string=="Obama_faked_birth"
replace CTI_num=7 if CTI_string=="Clinton_nukes"
replace CTI_num=8 if CTI_string=="COVID_exaggerated"
replace CTI_num=9 if CTI_string=="COVID_spread_purpose"
replace CTI_num=10 if CTI_string=="COVID_vaccine"
replace CTI_num=11 if CTI_string=="deep_state"
replace CTI_num=12 if CTI_string=="vaccine_dang_hidden"
replace CTI_num=13 if CTI_string=="UFOs_gov_hides"
replace CTI_num=14 if CTI_string=="COVID_tracking"
replace CTI_num=15 if CTI_string=="moon_landing_fake"
replace CTI_num=16 if CTI_string=="Simpson_framed"
replace CTI_num=17 if CTI_string=="one_percent"
replace CTI_num=18 if CTI_string=="QAnon_believer"
replace CTI_num=19 if CTI_string=="RFK_assassin"
replace CTI_num=20 if CTI_string=="Rothschilds_control"
replace CTI_num=21 if CTI_string=="Sandy_Hook"
replace CTI_num=22 if CTI_string=="Gov_assassin"
replace CTI_num=23 if CTI_string=="JFK_assassin"
replace CTI_num=24 if CTI_string=="kill_police"
replace CTI_num=25 if CTI_string=="lightbulbs"
replace CTI_num=26 if CTI_string=="mind_control_TV"
replace CTI_num=27 if CTI_string=="MLK_assassin"
replace CTI_num=28 if CTI_string=="epstein"
replace CTI_num=29 if CTI_string=="FDA_cancer"
replace CTI_num=30 if CTI_string=="FDR_lied"
replace CTI_num=31 if CTI_string=="Fluoride_sinister"
replace CTI_num=32 if CTI_string=="GMOs_dangers"
replace CTI_num=33 if CTI_string=="phones_cancer"
replace CTI_num=34 if CTI_string=="5G_COVID"
replace CTI_num=35 if CTI_string=="Sept11_truther"
replace CTI_num=36 if CTI_string=="AIDS_minorities"
replace CTI_num=37 if CTI_string=="big_pharma"
replace CTI_num=38 if CTI_string=="Bill_Gates"
replace CTI_num=39 if CTI_string=="bin_Laden"


label var CTI_num "Conspiracy theory number"
label define CTI_num 1 "Republican steal election" 2 "Elite trafficking" ///
				 	 3 "Iran hostage deal" 4 "Soros" 5 "Global warming hoax" ///
					 6 "Obama faked birth" 7 "Clinton nukes" ///
					 8 "COVID deaths exaggerated" 9 "COVID spread on purpose" ///
					 10 "COVID vaccine danger" 11 "Deep state" ///
					 12 "Vaccines dangers hidden" 13 "UFOs hidden" ///
					 14 "COVID install tracking" 15 "Moon landing faked" ///
					 16 "O.J. Simpson framed" 17 "The 1% control Gov." ///
					 18 "QAnon" 19 "Robert Kennedy assassin." ///
					 20 "Rothschilds" 21 "Sandy Hook shooting" ///
					 22 "Gov. assassin." 23 "JFK assassin." ///
					 24 "Kill police" 25 "Fluorescent light bulbs" ///
					 26 "Mind control via TV" 27 "MLK assassin." ///
					 28 "Epstein murdered" 29 "FDA cancer" ///
					 30 "FDR lied" 31 "Fluoride-water sinister" ///
					 32 "GMOs dangers" 33 "Phones cause cancer" ///
					 34 "5G tech. spread COVID" 35 "9/11 truther" ///
					 36 "AIDS harms minorities" 37 "Big pharma" ///
					 38 "Bill Gates COVID" 39 "bin Laden alive" 					 
label values CTI_num CTI_num
fre CTI_num

* The DV: Belief in the CT
rename CTI_ belief_CT
tab belief_CT
tab CTI_num belief_CT if rep==1, chi row
tab CTI_num belief_CT if rep==0, chi row


* "Congenial bloc" dummy
gen congenial_bloc=1 if CTI_num==1 & rep==0
replace congenial_bloc=0 if CTI_num==1 & rep==1
replace congenial_bloc=1 if CTI_num==2 & rep==1
replace congenial_bloc=0 if CTI_num==2 & rep==0
replace congenial_bloc=1 if CTI_num==3 & rep==0
replace congenial_bloc=0 if CTI_num==3 & rep==1
replace congenial_bloc=1 if CTI_num==4 & rep==1
replace congenial_bloc=0 if CTI_num==4 & rep==0
replace congenial_bloc=1 if CTI_num==5 & rep==1
replace congenial_bloc=0 if CTI_num==5 & rep==0
replace congenial_bloc=1 if CTI_num==6 & rep==1
replace congenial_bloc=0 if CTI_num==6 & rep==0
replace congenial_bloc=1 if CTI_num==7 & rep==1
replace congenial_bloc=0 if CTI_num==7 & rep==0
replace congenial_bloc=1 if CTI_num==8 & rep==1
replace congenial_bloc=0 if CTI_num==8 & rep==0
replace congenial_bloc=1 if CTI_num==9 & rep==1
replace congenial_bloc=0 if CTI_num==9 & rep==0
replace congenial_bloc=1 if CTI_num==10 & rep==1
replace congenial_bloc=0 if CTI_num==10 & rep==0
replace congenial_bloc=1 if CTI_num==11 & rep==1
replace congenial_bloc=0 if CTI_num==11 & rep==0
replace congenial_bloc=1 if CTI_num==12 & rep==1
replace congenial_bloc=0 if CTI_num==12 & rep==0
label var congenial_bloc "Congenial bloc"
tab congenial_bloc
tab CTI_num congenial_bloc if rep==1
tab CTI_num congenial_bloc if rep==0


* Table 1 - "Partisan/ideological" CTs analyses in the US
reg belief_CT c.CT_scale##i.congenial_bloc i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13, cluster(id)
outreg2 using Table_1.doc, replace se dec(3) alpha (.001, .01, .05) ///
symbol (***, **, *) keep(c.CT_scale##i.congenial_bloc) ///
ctitle ("Model 1 - Study 1") ///
addtext(CT Fixed-effects, "YES", Individual-level controls, "YES") ///
title("Table 1. Predicting belief in partisan conspiracy theories - US studies")


* Post-hoc power analysis
retrodesign .1617531, se(.034488) alpha(0.05) /*Power=.997*/

* Calculating the coef. of the CT scale among those in the "congenial bloc"
gen inter=CT_scale*congenial_bloc
reg belief_CT CT_scale congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13, cluster(id)
lincom CT_scale+inter
drop inter


* Table 3 - "Non-partisan" CTs analyses in the US/Israel
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num>12, cluster(id)
outreg2 using Table_3.doc, replace se dec(3) alpha (.001, .01, .05) ///
symbol (***, **, *) keep(c.CT_scale##i.rep) ///
ctitle ("Model 1 - Study 1") ///
addtext(CT Fixed-effects, "YES", Individual-level controls, "YES") ///
title ("Table 3. Predicting belief in non-partisan conspiracy theories - US/Israel studies")

* Calculating the coef. of the CT scale among Republicans
gen inter=CT_scale*rep
reg belief_CT CT_scale rep inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num>12, cluster(id)
lincom CT_scale+inter
drop inter


** Predicting specific "partisan" conspiracy theories **

* 1st "partisan" conspiracy theory - Republicans stole elections
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==1, r /*p<.001, in the expected direction*/

* 2nd "partisan" conspiracy theory - Elite trafficking
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==2, r /*p=.107, in the expected direction*/

* 3nd "partisan" conspiracy theory - Reagan/Iran Hostage deal
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==3, r /*p<.001, in the expected direction*/

* 4th "partisan" conspiracy theory - George Soros
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==4, r /*p=.003, in the expected direction*/

* 5th "partisan" conspiracy theory - Global warming is a hoax
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==5, r /*p=.057, in the expected direction*/

* 6th "partisan" conspiracy theory - Obama faked his citizenship ("Birther")
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==6, r /*p<.001, in the expected direction*/

* 7th "partisan" conspiracy theory - Clinton nukes to Russians
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==7, r /*p=.001, in the expected direction*/

* 8th "partisan" conspiracy theory - COVID-19 exaggerated
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==8, r /*p=.207, in the expected direction*/

* 9th "partisan" conspiracy theory - COVID-19 spread/released on purpose
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==9, r /*p=.001, in the expected direction*/

* 10th "partisan" conspiracy theory - COVID-19 vaccine dangerous 
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==10, r /*p=.032, in the expected direction*/

* 11th "partisan" conspiracy theory - There's a "deep state" in government  
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==11, r /*p=.057, in the expected direction*/

* 12th "partisan" conspiracy theory - The dangers of vaccines are hidden  
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==12, r /*p=.280, in the expected direction*/

/* 
In total, all 12 interactions in the expected direction ///
7/12 statistically significant (p<.05), 2/12 "marginally" significant (p<.1) ///
and 3/12 insignificant (p>.1)
*/


** The "non-partisan" conspiracy theories **

* 1st "non-partisan" conspiracy theory - info. on UFOs hidden by Gov.
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==13, r /*p=.178, stronger among Republicans*/

* 2nd "non-partisan" conspiracy theory - COVID-19 used to install tracking devices
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==14, r /*p=.535, stronger among Republicans*/

* 3rd "non-partisan" conspiracy theory - Moon landing faked
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==15, r /*p=.016, stronger among Democrats*/

* 4th "non-partisan" conspiracy theory - O.J. Simpson framed by police
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==16, r /*p<.001, stronger among Democrats*/

* 5th "non-partisan" conspiracy theory - The 1% of richest control the Gov.
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==17, r /*p<.001, stronger among Republicans*/

* 6th "non-partisan" conspiracy theory - believer in QANON
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==18, r /*p=.732, stronger among Democrats*/

* 7th "non-partisan" conspiracy theory - the assassination of Sen. Robert (Bobby) Kennedy 
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==19, r /*p=.079, stronger among Republicans*/

* 8th "non-partisan" conspiracy theory - the Rothschilds controls Gov., war, etc.
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==20, r /*p=.367, stronger among Republicans*/

* 9th "non-partisan" conspiracy theory - truth about Sandy Hook school shooting hidden
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==21, r /*p=.313, stronger among Republicans*/

* 10th "non-partisan" conspiracy theory - Gov. assassination of entertainers 
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==22, r /*p=.336, stronger among Democrats*/

* 11th "non-partisan" conspiracy theory - The assassination of JFK 
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==23, r /*p=.629, stronger among Republicans*/

* 12th "non-partisan" conspiracy theory - a national conspiracy to kill police 
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==24, r /*p<.001, stronger among Republicans*/

* 13th "non-partisan" conspiracy theory - fluorescent lightbulbs make people more obedient 
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==25, r /*p=.004, stronger among Democrats*/

* 14th "non-partisan" conspiracy theory - mind-controlling technology in TV broadcast 
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==26, r /*p=.215, stronger among Republicans*/

* 15th "non-partisan" conspiracy theory - the assassination of Martin Luther King (MLK) 
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==27, r /*p=.486, stronger among Republicans*/

* 16th "non-partisan" conspiracy theory - Epstein was murdered as part of a cover-up  
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==28, r /*p=.227, stronger among Republicans*/

* 17th "non-partisan" conspiracy theory - FDA preventing public from getting natural cures for cancer 
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==29, r /*p=.414, stronger among Republicans*/

* 18th "non-partisan" conspiracy theory - FDR knew about Pearl Harbor?
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==30, r /*p=.419, stronger among Republicans*/

* 19th "non-partisan" conspiracy theory - Fluoride in water supply for sinister reasons
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==31, r /*p=.347, stronger among Democrats*/

* 20th "non-partisan" conspiracy theory - dangers of GMO foods hidden 
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==32, r /*p=.836, stronger among Democrats*/

* 21th "non-partisan" conspiracy theory - phones cause cancer  
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==33, r /*p=.081, stronger among Democrats*/

* 22th "non-partisan" conspiracy theory - 5G technology spread COVID  
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==34, r /*p=.016, stronger among Democrats*/

* 23th "non-partisan" conspiracy theory - 9/11 an 'inside job' (truther) 
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==35, r /*p=.043, stronger among Democrats*/

* 24th "non-partisan" conspiracy theory - AIDS harms minorities
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==36, r /*p<.001, stronger among Democrats*/

* 25th "non-partisan" conspiracy theory - Big Pharma "invents" new diseases
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==37, r /*p=.354, stronger among Republicans*/

* 26th "non-partisan" conspiracy theory - Bill Gates is behind COVID-19
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==38, r /*p=.015, stronger among Republicans*/

* 27th "non-partisan" conspiracy theory - Osama bin Laden still alive
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group if CTI_num==39, r /*p=.337, stronger among Democrats*/

/* 
In total, 9/27 statistically significant (p<.05; 3 "for" Reps. + 6 "for" Dems.) ///
2/27 "marginally" significant (p<.1; 1 "for" Reps. + 1 "for" Dems.) ///
and 16/27 insignificant (p>.1)
*/


************************************
** Full results - Online Appendix **
************************************

* Table C1 - "partisan" CTs analyses in the US
reg belief_CT c.CT_scale##i.congenial_bloc i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13, cluster(id)
outreg2 using TableC1.doc, replace se dec(3) alpha (.001, .01, .05) ///
symbol (***, **, *) drop(i.CTI_num) ///
ctitle ("Model 1 - Study 1") addtext(CT Fixed-effects, "YES") ///
title ("Table C1. Predicting belief in partisan conspiracy theories, US studies – Full results") 

** Table C3 - "Non-partisan" conspiracy theory in Study 1 **
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num>12, cluster(id)
outreg2 using TableC3.doc, replace se dec(3) alpha (.001, .01, .05) ///
symbol (***, **, *) drop(i.CTI_num) ctitle ("Model 1 - Study 1") ///
addtext(CT Fixed-effects, "YES") ///
title ("Table C3. Predicting belief in non-partisan conspiracy theories in the US - Full results") 


*********************************************
** Dropping all controls - Online Appendix **
*********************************************

* Table D1 - "partisan/ideological" CT analyses in the US
reg belief_CT c.CT_scale##i.congenial_bloc i.CTI_num if CTI_num<13, cluster(id)
outreg2 using TableD1.doc, replace se dec(3) alpha (.001, .01, .05) ///
symbol (***, **, *) keep(c.CT_scale##i.congenial_bloc) ///
ctitle ("Model 1 - Study 1") addtext(CT Fixed-effects, "YES") ///
title ("Table D1. Predicting belief in partisan conspiracy theories, US studies – no controls") 

* Calculating the coef. of the CT scale among Republicans
gen inter=CT_scale*congenial_bloc
reg belief_CT CT_scale congenial_bloc inter i.CTI_num if CTI_num<13, cluster(id)
lincom CT_scale+inter
drop inter


********************************************
** "Jackknife" analysis - Online Appendix **
********************************************

** Running the main analysis, each time dropping 1 "partisan" CT **

gen inter=CT_scale*congenial_bloc

* Without the 1st "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1, cluster(id) /*b=.175; p=.000*/
scalar b1 = _b[inter]
scalar p1 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1: Coef=" %6.3f b1 "   p=" %6.3f p1

* Without the 2nd "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2, cluster(id) /*b=.171; p=.000*/
scalar b2 = _b[inter]
scalar p2 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2: Coef=" %6.3f b2 "   p=" %6.3f p2

* Without the 3rd "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3, cluster(id) 
scalar b3 = _b[inter]
scalar p3 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4, cluster(id)
scalar b4 = _b[inter]
scalar p4 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5, cluster(id) 
scalar b5 = _b[inter]
scalar p5 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6, cluster(id)
scalar b6 = _b[inter]
scalar p6 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 7th "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7, cluster(id) 
scalar b7 = _b[inter]
scalar p7 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 8th "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=8, cluster(id)
scalar b8 = _b[inter]
scalar p8 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 9th "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=9, cluster(id) 
scalar b9 = _b[inter]
scalar p9 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 10th "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=10, cluster(id)
scalar b10 = _b[inter]
scalar p10 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 11th "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=11, cluster(id) 
scalar b11 = _b[inter]
scalar p11 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 12th "partisan" CT
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=12, cluster(id)
scalar b12 = _b[inter]
scalar p12 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))


gen b_inter=.
gen p_inter=.
replace b_inter=b1 in 1
replace b_inter=b2 in 2
replace b_inter=b3 in 3
replace b_inter=b4 in 4
replace b_inter=b5 in 5
replace b_inter=b6 in 6
replace b_inter=b7 in 7
replace b_inter=b8 in 8
replace b_inter=b9 in 9
replace b_inter=b10 in 10
replace b_inter=b11 in 11
replace b_inter=b12 in 12
replace p_inter=p1 in 1
replace p_inter=p2 in 2
replace p_inter=p3 in 3
replace p_inter=p4 in 4
replace p_inter=p5 in 5
replace p_inter=p6 in 6
replace p_inter=p7 in 7
replace p_inter=p8 in 8
replace p_inter=p9 in 9
replace p_inter=p10 in 10
replace p_inter=p11 in 11
replace p_inter=p12 in 12

* A histogram of the 'jackknife' coefficients
twoway hist b_inter, bin(8) xscale(range(0 .25)) ///
xlabel(0(.05).25, labsize(medium)) scheme(s2mono) legend(off) ///
ylabel(0(10)80, angle(0) labsize(medium)) ///
graphregion(fcolor(white)) plotregion(margin(5 5 2 2)) /// 
xtitle("The interaction coefficient", size(medium)) ///
title(" ", size(medium))
/* || kdensity b_inter */
/* title("Study 1 estimates: Each time dropping one conspiracy theory"  */

sum b_inter p_inter
drop b_inter p_inter


** Running the main analysis, each time dropping 2 "partisan" CTs **

* Without the 1st and 2nd "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=2, cluster(id) /*b=.183; p=.000*/
scalar b1 = _b[inter]
scalar p1 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1&2: Coef=" %6.3f b1 "   p=" %6.3f p1

* Without the 1st and 3rd "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=3, cluster(id)
scalar b2 = _b[inter]
scalar p2 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st and 4th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=4, cluster(id)
scalar b3 = _b[inter]
scalar p3 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st and 5rd "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=5, cluster(id) 
scalar b4 = _b[inter]
scalar p4 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=6, cluster(id) 
scalar b5 = _b[inter]
scalar p5 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=7, cluster(id) 
scalar b6 = _b[inter]
scalar p6 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=8, cluster(id)
scalar b7 = _b[inter]
scalar p7 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=9, cluster(id)
scalar b8 = _b[inter]
scalar p8 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=10, cluster(id)
scalar b9 = _b[inter]
scalar p9 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=11, cluster(id)
scalar b10 = _b[inter]
scalar p10 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=12, cluster(id)
scalar b11 = _b[inter]
scalar p11 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd and 3rd "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=3, cluster(id) /*b=.200; p=.000*/
scalar b12 = _b[inter]
scalar p12 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2&3: Coef=" %6.3f b11 "   p=" %6.3f p11

* Without the 2nd and 4th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=4, cluster(id) /*b=.165; p=.000*/
scalar b13 = _b[inter]
scalar p13 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2&3: Coef=" %6.3f b12 "   p=" %6.3f p12

* Without the 2nd and 5th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=5, cluster(id) /*b=.200; p=.000*/
scalar b14 = _b[inter]
scalar p14 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=6, cluster(id) /*b=.165; p=.000*/
scalar b15 = _b[inter]
scalar p15 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=7, cluster(id) /*b=.200; p=.000*/
scalar b16 = _b[inter]
scalar p16 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=8, cluster(id) /*b=.165; p=.000*/
scalar b17 = _b[inter]
scalar p17 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=9, cluster(id) /*b=.200; p=.000*/
scalar b18 = _b[inter]
scalar p18 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=10, cluster(id) /*b=.165; p=.000*/
scalar b19 = _b[inter]
scalar p19 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=11, cluster(id) /*b=.200; p=.000*/
scalar b20 = _b[inter]
scalar p20 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=12, cluster(id) /*b=.165; p=.000*/
scalar b21 = _b[inter]
scalar p21 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd and 4th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=4, cluster(id) /*b=.168; p=.000*/
scalar b22 = _b[inter]
scalar p22 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=3&4: Coef=" %6.3f b22 "   p=" %6.3f p22

* Without the 3rd and 5th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=5, cluster(id) /*b=.174; p=.000*/
scalar b23 = _b[inter]
scalar p23 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=3&5: Coef=" %6.3f b23 "   p=" %6.3f p23

* Without the 3rd and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=6, cluster(id)
scalar b24 = _b[inter]
scalar p24 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=7, cluster(id) 
scalar b25 = _b[inter]
scalar p25 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=8, cluster(id)
scalar b26 = _b[inter]
scalar p26 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=9, cluster(id) 
scalar b27 = _b[inter]
scalar p27 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=10, cluster(id) 
scalar b28 = _b[inter]
scalar p28 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=11, cluster(id)
scalar b29 = _b[inter]
scalar p29 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=12, cluster(id) 
scalar b30 = _b[inter]
scalar p30 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th and 5th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=5, cluster(id) 
scalar b31 = _b[inter]
scalar p31 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=4&5: Coef=" %6.3f b31 "   p=" %6.3f p31

* Without the 4th and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=6, cluster(id) 
scalar b32 = _b[inter]
scalar p32 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=4&6: Coef=" %6.3f b32 "   p=" %6.3f p32

* Without the 4th and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=7, cluster(id)
scalar b33 = _b[inter]
scalar p33 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=8, cluster(id)
scalar b34 = _b[inter]
scalar p34 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=9, cluster(id)
scalar b35 = _b[inter]
scalar p35 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=10, cluster(id)
scalar b36 = _b[inter]
scalar p36 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=11, cluster(id)
scalar b37 = _b[inter]
scalar p37 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=12, cluster(id)
scalar b38 = _b[inter]
scalar p38 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=6, cluster(id) 
scalar b39 = _b[inter]
scalar p39 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=5&6: Coef=" %6.3f b39 "   p=" %6.3f p39

* Without the 5th and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=7, cluster(id) 
scalar b40 = _b[inter]
scalar p40 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=5&7: Coef=" %6.3f b40 "   p=" %6.3f p40

* Without the 5th and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=8, cluster(id) 
scalar b41 = _b[inter]
scalar p41 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=9, cluster(id) 
scalar b42 = _b[inter]
scalar p42 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=10, cluster(id) 
scalar b43 = _b[inter]
scalar p43 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=11, cluster(id) 
scalar b44 = _b[inter]
scalar p44 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=12, cluster(id) 
scalar b45 = _b[inter]
scalar p45 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=7, cluster(id) 
scalar b46 = _b[inter]
scalar p46 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=6&7: Coef=" %6.3f b46 "   p=" %6.3f p46

* Without the 6th and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=8, cluster(id) 
scalar b47 = _b[inter]
scalar p47 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=9, cluster(id) 
scalar b48 = _b[inter]
scalar p48 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=10, cluster(id) 
scalar b49 = _b[inter]
scalar p49 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=11, cluster(id) 
scalar b50 = _b[inter]
scalar p50 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=12, cluster(id) 
scalar b51 = _b[inter]
scalar p51 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 7th and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=8, cluster(id) 
scalar b52 = _b[inter]
scalar p52 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=7&8: Coef=" %6.3f b52 "   p=" %6.3f p52

* Without the 7th and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=9, cluster(id) 
scalar b53 = _b[inter]
scalar p53 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 7th and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=10, cluster(id) 
scalar b54 = _b[inter]
scalar p54 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 7th and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=11, cluster(id) 
scalar b55 = _b[inter]
scalar p55 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 7th and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=12, cluster(id) 
scalar b56 = _b[inter]
scalar p56 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 8th and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=8 & CTI_num!=9, cluster(id) 
scalar b57 = _b[inter]
scalar p57 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=8&9: Coef=" %6.3f b57 "   p=" %6.3f p57

* Without the 8th and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=8 & CTI_num!=10, cluster(id) 
scalar b58 = _b[inter]
scalar p58 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 8th and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=8 & CTI_num!=11, cluster(id) 
scalar b59 = _b[inter]
scalar p59 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 8th and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=8 & CTI_num!=12, cluster(id) 
scalar b60 = _b[inter]
scalar p60 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 9th and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=9 & CTI_num!=10, cluster(id) 
scalar b61 = _b[inter]
scalar p61 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=9&10: Coef=" %6.3f b61 "   p=" %6.3f p61

* Without the 9th and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=9 & CTI_num!=11, cluster(id) 
scalar b62 = _b[inter]
scalar p62 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 9th and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=9 & CTI_num!=12, cluster(id) 
scalar b63 = _b[inter]
scalar p63 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 10th and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=10 & CTI_num!=11, cluster(id) 
scalar b64 = _b[inter]
scalar p64 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=10&11: Coef=" %6.3f b64 "   p=" %6.3f p64

* Without the 10th and 12h "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=10 & CTI_num!=12, cluster(id) 
scalar b65 = _b[inter]
scalar p65 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 11th and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=11 & CTI_num!=12, cluster(id) 
scalar b66 = _b[inter]
scalar p66 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=11&12: Coef=" %6.3f b66 "   p=" %6.3f p66

gen b_inter=.
gen p_inter=.
replace b_inter=b1 in 1
replace b_inter=b2 in 2
replace b_inter=b3 in 3
replace b_inter=b4 in 4
replace b_inter=b5 in 5
replace b_inter=b6 in 6
replace b_inter=b7 in 7
replace b_inter=b8 in 8
replace b_inter=b9 in 9
replace b_inter=b10 in 10
replace b_inter=b11 in 11
replace b_inter=b12 in 12
replace b_inter=b13 in 13
replace b_inter=b14 in 14
replace b_inter=b15 in 15
replace b_inter=b16 in 16
replace b_inter=b17 in 17
replace b_inter=b18 in 18
replace b_inter=b19 in 19
replace b_inter=b20 in 20
replace b_inter=b21 in 21
replace b_inter=b22 in 22
replace b_inter=b23 in 23
replace b_inter=b24 in 24
replace b_inter=b25 in 25
replace b_inter=b26 in 26
replace b_inter=b27 in 27
replace b_inter=b28 in 28
replace b_inter=b29 in 29
replace b_inter=b30 in 30
replace b_inter=b31 in 31
replace b_inter=b32 in 32
replace b_inter=b33 in 33
replace b_inter=b34 in 34
replace b_inter=b35 in 35
replace b_inter=b36 in 36
replace b_inter=b37 in 37
replace b_inter=b38 in 38
replace b_inter=b39 in 39
replace b_inter=b40 in 40
replace b_inter=b41 in 41
replace b_inter=b42 in 42
replace b_inter=b43 in 43
replace b_inter=b44 in 44
replace b_inter=b45 in 45
replace b_inter=b46 in 46
replace b_inter=b47 in 47
replace b_inter=b48 in 48
replace b_inter=b49 in 49
replace b_inter=b50 in 50
replace b_inter=b51 in 51
replace b_inter=b52 in 52
replace b_inter=b53 in 53
replace b_inter=b54 in 54
replace b_inter=b55 in 55
replace b_inter=b56 in 56
replace b_inter=b57 in 57
replace b_inter=b58 in 58
replace b_inter=b59 in 59
replace b_inter=b60 in 60
replace b_inter=b61 in 61
replace b_inter=b62 in 62
replace b_inter=b63 in 63
replace b_inter=b64 in 64
replace b_inter=b65 in 65
replace b_inter=b66 in 66

replace p_inter=p1 in 1
replace p_inter=p2 in 2
replace p_inter=p3 in 3
replace p_inter=p4 in 4
replace p_inter=p5 in 5
replace p_inter=p6 in 6
replace p_inter=p7 in 7
replace p_inter=p8 in 8
replace p_inter=p9 in 9
replace p_inter=p10 in 10
replace p_inter=p11 in 11
replace p_inter=p12 in 12
replace p_inter=p13 in 13
replace p_inter=p14 in 14
replace p_inter=p15 in 15
replace p_inter=p16 in 16
replace p_inter=p17 in 17
replace p_inter=p18 in 18
replace p_inter=p19 in 19
replace p_inter=p20 in 20
replace p_inter=p21 in 21
replace p_inter=p22 in 22
replace p_inter=p23 in 23
replace p_inter=p24 in 24
replace p_inter=p25 in 25
replace p_inter=p26 in 26
replace p_inter=p27 in 27
replace p_inter=p28 in 28
replace p_inter=p29 in 29
replace p_inter=p30 in 30
replace p_inter=p31 in 31
replace p_inter=p32 in 32
replace p_inter=p33 in 33
replace p_inter=p34 in 34
replace p_inter=p35 in 35
replace p_inter=p36 in 36
replace p_inter=p37 in 37
replace p_inter=p38 in 38
replace p_inter=p39 in 39
replace p_inter=p40 in 40
replace p_inter=p41 in 41
replace p_inter=p42 in 42
replace p_inter=p43 in 43
replace p_inter=p44 in 44
replace p_inter=p45 in 45
replace p_inter=p46 in 46
replace p_inter=p47 in 47
replace p_inter=p48 in 48
replace p_inter=p49 in 49
replace p_inter=p50 in 50
replace p_inter=p51 in 51
replace p_inter=p52 in 52
replace p_inter=p53 in 53
replace p_inter=p54 in 54
replace p_inter=p55 in 55
replace p_inter=p56 in 56
replace p_inter=p57 in 57
replace p_inter=p58 in 58
replace p_inter=p59 in 59
replace p_inter=p60 in 60
replace p_inter=p61 in 61
replace p_inter=p62 in 62
replace p_inter=p63 in 63
replace p_inter=p64 in 64
replace p_inter=p65 in 65
replace p_inter=p66 in 66

* A histogram of the 'jackknife' coefficients
twoway hist b_inter, bin(8) xscale(range(0 .25)) ///
xlabel(0(.05).25, labsize(medium)) scheme(s2mono) legend(off) ///
ylabel(0(10)40, angle(0) labsize(medium)) ///
graphregion(fcolor(white)) plotregion(margin(5 5 2 2)) /// 
xtitle("The interaction coefficient", size(medium)) ///
title(" ", size(medium))
/* || kdensity b_inter */
/* title("Study 1 estimates: Each time dropping two conspiracy theories") */

sum b_inter p_inter
drop b_inter p_inter


** Running the main analysis, each time dropping 3 "partisan" CTs **

* Without the 1st, 2nd, and 3rd "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=2 & CTI_num!=3, cluster(id) /*b=.208; p=.000*/
scalar b1 = _b[inter]
scalar p1 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1&2&3: Coef=" %6.3f b1 "   p=" %6.3f p1

* Without the 1st, 2nd, and 4th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=2 & CTI_num!=4, cluster(id) /*b=.177; p=.000*/
scalar b2 = _b[inter]
scalar p2 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 2nd, and 5th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=2 & CTI_num!=5, cluster(id) 
scalar b3 = _b[inter]
scalar p3 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 2nd, and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=2 & CTI_num!=6, cluster(id) 
scalar b4 = _b[inter]
scalar p4 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 2nd, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=2 & CTI_num!=7, cluster(id) 
scalar b5 = _b[inter]
scalar p5 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 2nd, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=2 & CTI_num!=8, cluster(id) 
scalar b6 = _b[inter]
scalar p6 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 2nd, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=2 & CTI_num!=9, cluster(id) 
scalar b7 = _b[inter]
scalar p7 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 2nd, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=2 & CTI_num!=10, cluster(id) 
scalar b8 = _b[inter]
scalar p8 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 2nd, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=2 & CTI_num!=11, cluster(id) 
scalar b9 = _b[inter]
scalar p9 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 2nd, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=2 & CTI_num!=12, cluster(id) 
scalar b10 = _b[inter]
scalar p10 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 3rd, and 4th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=3 & CTI_num!=4, cluster(id) /*b=.195; p=.000*/
scalar b11 = _b[inter]
scalar p11 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1&3&4: Coef=" %6.3f b11 "   p=" %6.3f p11

* Without the 1st, 3rd, and 5th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=3 & CTI_num!=5, cluster(id) 
scalar b12 = _b[inter]
scalar p12 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 3rd, and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=3 & CTI_num!=6, cluster(id) 
scalar b13 = _b[inter]
scalar p13 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 3rd, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=3 & CTI_num!=7, cluster(id) 
scalar b14 = _b[inter]
scalar p14 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 3rd, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=3 & CTI_num!=8, cluster(id) 
scalar b15 = _b[inter]
scalar p15 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 3rd, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=3 & CTI_num!=9, cluster(id) 
scalar b16 = _b[inter]
scalar p16 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 3rd, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=3 & CTI_num!=10, cluster(id) 
scalar b17 = _b[inter]
scalar p17 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 3rd, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=3 & CTI_num!=11, cluster(id) 
scalar b18 = _b[inter]
scalar p18 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 3rd, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=3 & CTI_num!=12, cluster(id) 
scalar b19 = _b[inter]
scalar p19 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 4th, and 5th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=4 & CTI_num!=5, cluster(id) /*b=.167; p=.000*/
scalar b20 = _b[inter]
scalar p20 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1&4&5: Coef=" %6.3f b20 "   p=" %6.3f p20

* Without the 1st, 4th, and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=4 & CTI_num!=6, cluster(id) 
scalar b21 = _b[inter]
scalar p21 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 4th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=4 & CTI_num!=7, cluster(id)
scalar b22 = _b[inter]
scalar p22 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 4th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=4 & CTI_num!=8, cluster(id)
scalar b23 = _b[inter]
scalar p23 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 4th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=4 & CTI_num!=9, cluster(id)
scalar b24 = _b[inter]
scalar p24 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 4th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=4 & CTI_num!=10, cluster(id)
scalar b25 = _b[inter]
scalar p25 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 4th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=4 & CTI_num!=11, cluster(id)
scalar b26 = _b[inter]
scalar p26 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 4th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=4 & CTI_num!=12, cluster(id)
scalar b27 = _b[inter]
scalar p27 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 5th, and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=5 & CTI_num!=6, cluster(id) /*b=.139; p=.000*/
scalar b28 = _b[inter]
scalar p28 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1&5&6: Coef=" %6.3f b28 "   p=" %6.3f p28

* Without the 1st, 5th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=5 & CTI_num!=7, cluster(id) 
scalar b29 = _b[inter]
scalar p29 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 5th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=5 & CTI_num!=8, cluster(id) 
scalar b30 = _b[inter]
scalar p30 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 5th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=5 & CTI_num!=9, cluster(id) 
scalar b31 = _b[inter]
scalar p31 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 5th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=5 & CTI_num!=10, cluster(id) 
scalar b32 = _b[inter]
scalar p32 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 5th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=5 & CTI_num!=11, cluster(id) 
scalar b33 = _b[inter]
scalar p33 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 5th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=5 & CTI_num!=12, cluster(id) 
scalar b34 = _b[inter]
scalar p34 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 6th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=6 & CTI_num!=7, cluster(id) /*b=.128; p=.001*/
scalar b35 = _b[inter]
scalar p35 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1&6&7: Coef=" %6.3f b35 "   p=" %6.3f p35

* Without the 1st, 6th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=6 & CTI_num!=8, cluster(id)
scalar b36 = _b[inter]
scalar p36 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 6th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=6 & CTI_num!=9, cluster(id)
scalar b37 = _b[inter]
scalar p37 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 6th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=6 & CTI_num!=10, cluster(id)
scalar b38 = _b[inter]
scalar p38 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 6th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=6 & CTI_num!=11, cluster(id)
scalar b39 = _b[inter]
scalar p39 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 6th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=6 & CTI_num!=12, cluster(id)
scalar b40 = _b[inter]
scalar p40 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 7th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=7 & CTI_num!=8, cluster(id) /*b=.171; p=.000*/
scalar b41 = _b[inter]
scalar p41 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1&7&8: Coef=" %6.3f b41 "   p=" %6.3f p41

* Without the 1st, 7th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=7 & CTI_num!=9, cluster(id) 
scalar b42 = _b[inter]
scalar p42 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 7th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=7 & CTI_num!=10, cluster(id) 
scalar b43 = _b[inter]
scalar p43 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 7th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=7 & CTI_num!=11, cluster(id) 
scalar b44 = _b[inter]
scalar p44 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 7th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=7 & CTI_num!=12, cluster(id) 
scalar b45 = _b[inter]
scalar p45 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 8th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=8 & CTI_num!=9, cluster(id) /*b=.176; p=.000*/
scalar b46 = _b[inter]
scalar p46 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1&8&9: Coef=" %6.3f b46 "   p=" %6.3f p46

* Without the 1st, 8th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=8 & CTI_num!=10, cluster(id) 
scalar b47 = _b[inter]
scalar p47 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 8th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=8 & CTI_num!=11, cluster(id) 
scalar b48 = _b[inter]
scalar p48 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 8th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=8 & CTI_num!=12, cluster(id)
scalar b49 = _b[inter]
scalar p49 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 9th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=9 & CTI_num!=10, cluster(id) /*b=.170; p=.000*/
scalar b50 = _b[inter]
scalar p50 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1&9&10: Coef=" %6.3f b50 "   p=" %6.3f p50

* Without the 1st, 9th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=9 & CTI_num!=11, cluster(id)
scalar b51 = _b[inter]
scalar p51 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 9th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=9 & CTI_num!=12, cluster(id)
scalar b52 = _b[inter]
scalar p52 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 10th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=10 & CTI_num!=11, cluster(id) /*b=.186; p=.000*/
scalar b53 = _b[inter]
scalar p53 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1&10&11: Coef=" %6.3f b53 "   p=" %6.3f p53

* Without the 1st, 10th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=10 & CTI_num!=12, cluster(id) 
scalar b54 = _b[inter]
scalar p54 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 1st, 11th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=1 & CTI_num!=11 & CTI_num!=12, cluster(id) /*b=.186; p=.000*/
scalar b55 = _b[inter]
scalar p55 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=1&11&12: Coef=" %6.3f b55 "   p=" %6.3f p55

* Without the 2nd, 3rd, and 4th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=3 & CTI_num!=4, cluster(id) /*b=.177; p=.000*/
scalar b56 = _b[inter]
scalar p56 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2&3&4: Coef=" %6.3f b56 "   p=" %6.3f p56

* Without the 2nd, 3rd, and 5th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=3 & CTI_num!=5, cluster(id) 
scalar b57 = _b[inter]
scalar p57 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 3rd, and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=3 & CTI_num!=6, cluster(id) 
scalar b58 = _b[inter]
scalar p58 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 3rd, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=3 & CTI_num!=7, cluster(id) 
scalar b59 = _b[inter]
scalar p59 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 3rd, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=3 & CTI_num!=8, cluster(id) 
scalar b60 = _b[inter]
scalar p60 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 3rd, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=3 & CTI_num!=9, cluster(id) 
scalar b61 = _b[inter]
scalar p61 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 3rd, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=3 & CTI_num!=10, cluster(id) 
scalar b62 = _b[inter]
scalar p62 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 3rd, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=3 & CTI_num!=11, cluster(id) 
scalar b63 = _b[inter]
scalar p63 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 3rd, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=3 & CTI_num!=12, cluster(id) 
scalar b64 = _b[inter]
scalar p64 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 4th, and 5th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=4 & CTI_num!=5, cluster(id) 
scalar b65 = _b[inter]
scalar p65 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2&4&5: Coef=" %6.3f b65 "   p=" %6.3f p65

* Without the 2nd, 4th, and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=4 & CTI_num!=6, cluster(id) 
scalar b66 = _b[inter]
scalar p66 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 4th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=4 & CTI_num!=7, cluster(id) 
scalar b67 = _b[inter]
scalar p67 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 4th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=4 & CTI_num!=8, cluster(id) 
scalar b68 = _b[inter]
scalar p68 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 4th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=4 & CTI_num!=9, cluster(id) 
scalar b69 = _b[inter]
scalar p69 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 4th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=4 & CTI_num!=10, cluster(id) 
scalar b70 = _b[inter]
scalar p70 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 4th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=4 & CTI_num!=11, cluster(id) 
scalar b71 = _b[inter]
scalar p71 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 4th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=4 & CTI_num!=12, cluster(id) 
scalar b72 = _b[inter]
scalar p72 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 5th, and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=5 & CTI_num!=6, cluster(id) 
scalar b73 = _b[inter]
scalar p73 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2&5&6: Coef=" %6.3f b73 "   p=" %6.3f p73

* Without the 2nd, 5th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=5 & CTI_num!=7, cluster(id) 
scalar b74 = _b[inter]
scalar p74 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 5th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=5 & CTI_num!=8, cluster(id) 
scalar b75 = _b[inter]
scalar p75 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 5th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=5 & CTI_num!=9, cluster(id) 
scalar b76 = _b[inter]
scalar p76 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 5th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=5 & CTI_num!=10, cluster(id) 
scalar b77 = _b[inter]
scalar p77 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 5th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=5 & CTI_num!=11, cluster(id) 
scalar b78 = _b[inter]
scalar p78 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 5th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=5 & CTI_num!=12, cluster(id) 
scalar b79 = _b[inter]
scalar p79 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 6th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=6 & CTI_num!=7, cluster(id) 
scalar b80 = _b[inter]
scalar p80 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2&6&7: Coef=" %6.3f b80 "   p=" %6.3f p80

* Without the 2nd, 6th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=6 & CTI_num!=8, cluster(id) 
scalar b81 = _b[inter]
scalar p81 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 6th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=6 & CTI_num!=9, cluster(id) 
scalar b82 = _b[inter]
scalar p82 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 6th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=6 & CTI_num!=10, cluster(id) 
scalar b83 = _b[inter]
scalar p83 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 6th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=6 & CTI_num!=11, cluster(id) 
scalar b84 = _b[inter]
scalar p84 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 6th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=6 & CTI_num!=12, cluster(id) 
scalar b85 = _b[inter]
scalar p85 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 7th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=7 & CTI_num!=8, cluster(id) 
scalar b86 = _b[inter]
scalar p86 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2&7&8: Coef=" %6.3f b86 "   p=" %6.3f p86

* Without the 2nd, 7th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=7 & CTI_num!=9, cluster(id) 
scalar b87 = _b[inter]
scalar p87 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 7th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=7 & CTI_num!=10, cluster(id) 
scalar b88 = _b[inter]
scalar p88 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 7th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=7 & CTI_num!=11, cluster(id) 
scalar b89 = _b[inter]
scalar p89 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 7th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=7 & CTI_num!=12, cluster(id) 
scalar b90 = _b[inter]
scalar p90 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 8th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=8 & CTI_num!=9, cluster(id) 
scalar b91 = _b[inter]
scalar p91 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2&8&9: Coef=" %6.3f b91 "   p=" %6.3f p91

* Without the 2nd, 8th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=8 & CTI_num!=10, cluster(id) 
scalar b92 = _b[inter]
scalar p92 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 8th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=8 & CTI_num!=11, cluster(id) 
scalar b93 = _b[inter]
scalar p93 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 8th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=8 & CTI_num!=12, cluster(id) 
scalar b94 = _b[inter]
scalar p94 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 9th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=9 & CTI_num!=10, cluster(id) 
scalar b95 = _b[inter]
scalar p95 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2&9&10: Coef=" %6.3f b95 "   p=" %6.3f p95

* Without the 2nd, 9th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=9 & CTI_num!=11, cluster(id) 
scalar b96 = _b[inter]
scalar p96 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 9th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=9 & CTI_num!=12, cluster(id) 
scalar b97 = _b[inter]
scalar p97 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 10th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=10 & CTI_num!=11, cluster(id) 
scalar b98 = _b[inter]
scalar p98 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2&10&11: Coef=" %6.3f b98 "   p=" %6.3f p98

* Without the 2nd, 10th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=10 & CTI_num!=12, cluster(id) 
scalar b99 = _b[inter]
scalar p99 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 2nd, 11th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=2 & CTI_num!=11 & CTI_num!=12, cluster(id) 
scalar b100 = _b[inter]
scalar p100 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=2&11&121: Coef=" %6.3f b100 "   p=" %6.3f b100

* Without the 3rd, 4th, and 5th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=4 & CTI_num!=5, cluster(id) 
scalar b101 = _b[inter]
scalar p101 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=3&4&5: Coef=" %6.3f b101 "   p=" %6.3f p101

* Without the 3rd, 4th, and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=4 & CTI_num!=6, cluster(id) 
scalar b102 = _b[inter]
scalar p102 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 4th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=4 & CTI_num!=7, cluster(id) 
scalar b103 = _b[inter]
scalar p103 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 4th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=4 & CTI_num!=8, cluster(id) 
scalar b104 = _b[inter]
scalar p104 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 4th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=4 & CTI_num!=9, cluster(id) 
scalar b105 = _b[inter]
scalar p105 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 4th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=4 & CTI_num!=10, cluster(id) 
scalar b106 = _b[inter]
scalar p106 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 4th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=4 & CTI_num!=11, cluster(id) 
scalar b107 = _b[inter]
scalar p107 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 4th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=4 & CTI_num!=12, cluster(id) 
scalar b108 = _b[inter]
scalar p108 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 5th, and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=5 & CTI_num!=6, cluster(id) 
scalar b109 = _b[inter]
scalar p109 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=3&5&6: Coef=" %6.3f b109 "   p=" %6.3f p109

* Without the 3rd, 5th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=5 & CTI_num!=7, cluster(id) 
scalar b110 = _b[inter]
scalar p110 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 5th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=5 & CTI_num!=8, cluster(id) 
scalar b111 = _b[inter]
scalar p111 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 5th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=5 & CTI_num!=9, cluster(id) 
scalar b112 = _b[inter]
scalar p112 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 5th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=5 & CTI_num!=10, cluster(id) 
scalar b113 = _b[inter]
scalar p113 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 5th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=5 & CTI_num!=11, cluster(id) 
scalar b114 = _b[inter]
scalar p114 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 5th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=5 & CTI_num!=12, cluster(id) 
scalar b115 = _b[inter]
scalar p115 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 6th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=6 & CTI_num!=7, cluster(id) 
scalar b116 = _b[inter]
scalar p116 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=3&6&7: Coef=" %6.3f b116 "   p=" %6.3f p116

* Without the 3rd, 6th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=6 & CTI_num!=8, cluster(id) 
scalar b117 = _b[inter]
scalar p117 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 6th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=6 & CTI_num!=9, cluster(id) 
scalar b118 = _b[inter]
scalar p118 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 6th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=6 & CTI_num!=10, cluster(id) 
scalar b119 = _b[inter]
scalar p119 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 6th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=6 & CTI_num!=11, cluster(id) 
scalar b120 = _b[inter]
scalar p120 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 6th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=6 & CTI_num!=12, cluster(id) 
scalar b121 = _b[inter]
scalar p121 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 7th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=7 & CTI_num!=8, cluster(id) 
scalar b122 = _b[inter]
scalar p122 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=3&7&8: Coef=" %6.3f b122 "   p=" %6.3f p122

* Without the 3rd, 7th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=7 & CTI_num!=9, cluster(id) 
scalar b123 = _b[inter]
scalar p123 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 7th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=7 & CTI_num!=10, cluster(id) 
scalar b124 = _b[inter]
scalar p124 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 7th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=7 & CTI_num!=11, cluster(id) 
scalar b125 = _b[inter]
scalar p125 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 7th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=7 & CTI_num!=12, cluster(id) 
scalar b126 = _b[inter]
scalar p126 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 8th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=8 & CTI_num!=9, cluster(id) 
scalar b127 = _b[inter]
scalar p127 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=3&8&9: Coef=" %6.3f b127 "   p=" %6.3f p127

* Without the 3rd, 8th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=8 & CTI_num!=10, cluster(id) 
scalar b128 = _b[inter]
scalar p128 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 8th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=8 & CTI_num!=11, cluster(id) 
scalar b129 = _b[inter]
scalar p129 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 8th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=8 & CTI_num!=12, cluster(id) 
scalar b130 = _b[inter]
scalar p130 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 9th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=9 & CTI_num!=10, cluster(id) 
scalar b131 = _b[inter]
scalar p131 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=3&9&10: Coef=" %6.3f b131 "   p=" %6.3f p131

* Without the 3rd, 9th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=9 & CTI_num!=11, cluster(id) 
scalar b132 = _b[inter]
scalar p132 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 9th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=9 & CTI_num!=12, cluster(id) 
scalar b133 = _b[inter]
scalar p133 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 10th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=10 & CTI_num!=11, cluster(id) 
scalar b134 = _b[inter]
scalar p134 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=3&10&11: Coef=" %6.3f b134 "   p=" %6.3f p134

* Without the 3rd, 10th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=10 & CTI_num!=12, cluster(id) 
scalar b135 = _b[inter]
scalar p135 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 3rd, 11th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=3 & CTI_num!=11 & CTI_num!=12, cluster(id) 
scalar b136 = _b[inter]
scalar p136 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=3&11&12: Coef=" %6.3f b136 "   p=" %6.3f p136

* Without the 4th, 5th, and 6th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=5 & CTI_num!=6, cluster(id) 
scalar b137 = _b[inter]
scalar p137 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=4&5&6: Coef=" %6.3f b137 "   p=" %6.3f p137

* Without the 4th, 5th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=5 & CTI_num!=7, cluster(id) 
scalar b138 = _b[inter]
scalar p138 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 5th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=5 & CTI_num!=7, cluster(id) 
scalar b139 = _b[inter]
scalar p139 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 5th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=5 & CTI_num!=9, cluster(id) 
scalar b140 = _b[inter]
scalar p140 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 5th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=5 & CTI_num!=10, cluster(id) 
scalar b141 = _b[inter]
scalar p141 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 5th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=5 & CTI_num!=11, cluster(id) 
scalar b142 = _b[inter]
scalar p142 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 5th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=5 & CTI_num!=12, cluster(id) 
scalar b143 = _b[inter]
scalar p143 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 6th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=6 & CTI_num!=7, cluster(id) 
scalar b144 = _b[inter]
scalar p144 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=4&6&7: Coef=" %6.3f b144 "   p=" %6.3f p144

* Without the 4th, 6th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=6 & CTI_num!=8, cluster(id) 
scalar b145 = _b[inter]
scalar p145 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 6th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=6 & CTI_num!=9, cluster(id) 
scalar b146 = _b[inter]
scalar p146 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 6th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=6 & CTI_num!=10, cluster(id) 
scalar b147 = _b[inter]
scalar p147 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 6th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=6 & CTI_num!=11, cluster(id) 
scalar b148 = _b[inter]
scalar p148 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 6th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=6 & CTI_num!=12, cluster(id) 
scalar b149 = _b[inter]
scalar p149 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 7th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=7 & CTI_num!=8, cluster(id) 
scalar b150 = _b[inter]
scalar p150 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=4&7&8: Coef=" %6.3f b150 "   p=" %6.3f p150

* Without the 4th, 7th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=7 & CTI_num!=9, cluster(id) 
scalar b151 = _b[inter]
scalar p151 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 7th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=7 & CTI_num!=10, cluster(id) 
scalar b152 = _b[inter]
scalar p152 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 7th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=7 & CTI_num!=11, cluster(id) 
scalar b153 = _b[inter]
scalar p153 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 7th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=7 & CTI_num!=12, cluster(id) 
scalar b154 = _b[inter]
scalar p154 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 8th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=8 & CTI_num!=9, cluster(id) 
scalar b155 = _b[inter]
scalar p155 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=4&8&9: Coef=" %6.3f b155 "   p=" %6.3f p155

* Without the 4th, 8th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=8 & CTI_num!=10, cluster(id) 
scalar b156 = _b[inter]
scalar p156 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 8th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=8 & CTI_num!=11, cluster(id) 
scalar b157 = _b[inter]
scalar p157 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 8th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=8 & CTI_num!=12, cluster(id) 
scalar b158 = _b[inter]
scalar p158 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 9th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=9 & CTI_num!=10, cluster(id) 
scalar b159 = _b[inter]
scalar p159 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=4&9&10: Coef=" %6.3f b159 "   p=" %6.3f p159

* Without the 4th, 9th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=9 & CTI_num!=11, cluster(id) 
scalar b160 = _b[inter]
scalar p160 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 9th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=9 & CTI_num!=12, cluster(id) 
scalar b161 = _b[inter]
scalar p161 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 10th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=10 & CTI_num!=11, cluster(id) 
scalar b162 = _b[inter]
scalar p162 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=4&10&11: Coef=" %6.3f b162 "   p=" %6.3f p162

* Without the 4th, 10th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=10 & CTI_num!=12, cluster(id) 
scalar b163 = _b[inter]
scalar p163 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 4th, 11th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=4 & CTI_num!=11 & CTI_num!=12, cluster(id) 
scalar b164 = _b[inter]
scalar p164 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=4&11&12: Coef=" %6.3f b164 "   p=" %6.3f p164

* Without the 5th, 6th, and 7th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=6 & CTI_num!=7, cluster(id) 
scalar b165 = _b[inter]
scalar p165 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=5&6&7: Coef=" %6.3f b165 "   p=" %6.3f p165

* Without the 5th, 6th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=6 & CTI_num!=8, cluster(id) 
scalar b166 = _b[inter]
scalar p166 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 6th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=6 & CTI_num!=9, cluster(id) 
scalar b167 = _b[inter]
scalar p167 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 6th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=6 & CTI_num!=10, cluster(id) 
scalar b168 = _b[inter]
scalar p168 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 6th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=6 & CTI_num!=11, cluster(id) 
scalar b169 = _b[inter]
scalar p169 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 6th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=6 & CTI_num!=12, cluster(id) 
scalar b170 = _b[inter]
scalar p170 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 7th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=7 & CTI_num!=8, cluster(id) 
scalar b171 = _b[inter]
scalar p171 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=5&7&8: Coef=" %6.3f b171 "   p=" %6.3f p171

* Without the 5th, 7th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=7 & CTI_num!=9, cluster(id) 
scalar b172 = _b[inter]
scalar p172 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 7th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=7 & CTI_num!=10, cluster(id) 
scalar b173 = _b[inter]
scalar p173 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 7th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=7 & CTI_num!=11, cluster(id) 
scalar b174 = _b[inter]
scalar p174 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 7th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=7 & CTI_num!=12, cluster(id) 
scalar b175 = _b[inter]
scalar p175 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 8th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=8 & CTI_num!=9, cluster(id) 
scalar b176 = _b[inter]
scalar p176 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=5&8&9: Coef=" %6.3f b176 "   p=" %6.3f p176

* Without the 5th, 8th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=8 & CTI_num!=10, cluster(id) 
scalar b177 = _b[inter]
scalar p177 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 8th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=8 & CTI_num!=11, cluster(id) 
scalar b178 = _b[inter]
scalar p178 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 8th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=8 & CTI_num!=12, cluster(id) 
scalar b179 = _b[inter]
scalar p179 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 9th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=9 & CTI_num!=10, cluster(id) 
scalar b180 = _b[inter]
scalar p180 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=5&9&10: Coef=" %6.3f b180 "   p=" %6.3f p180

* Without the 5th, 9th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=9 & CTI_num!=11, cluster(id) 
scalar b181 = _b[inter]
scalar p181 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 9th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=9 & CTI_num!=12, cluster(id) 
scalar b182 = _b[inter]
scalar p182 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 10th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=10 & CTI_num!=11, cluster(id) 
scalar b183 = _b[inter]
scalar p183 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=5&10&11: Coef=" %6.3f b183 "   p=" %6.3f p183

* Without the 5th, 10th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=10 & CTI_num!=12, cluster(id) 
scalar b184 = _b[inter]
scalar p184 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 5th, 11th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=5 & CTI_num!=11 & CTI_num!=12, cluster(id) 
scalar b185 = _b[inter]
scalar p185 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=5&11&12: Coef=" %6.3f b185 "   p=" %6.3f p185

* Without the 6th, 7th, and 8th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=7 & CTI_num!=8, cluster(id) 
scalar b186 = _b[inter]
scalar p186 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=6&7&8: Coef=" %6.3f b186 "   p=" %6.3f p186

* Without the 6th, 7th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=7 & CTI_num!=9, cluster(id) 
scalar b187 = _b[inter]
scalar p187 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th, 7th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=7 & CTI_num!=10, cluster(id) 
scalar b188 = _b[inter]
scalar p188 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th, 7th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=7 & CTI_num!=11, cluster(id) 
scalar b189 = _b[inter]
scalar p189 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th, 7th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=7 & CTI_num!=12, cluster(id) 
scalar b190 = _b[inter]
scalar p190 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th, 8th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=8 & CTI_num!=9, cluster(id) 
scalar b191 = _b[inter]
scalar p191 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=6&8&9: Coef=" %6.3f b191 "   p=" %6.3f p191

* Without the 6th, 8th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=8 & CTI_num!=10, cluster(id) 
scalar b192 = _b[inter]
scalar p192 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th, 8th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=8 & CTI_num!=11, cluster(id) 
scalar b193 = _b[inter]
scalar p193 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th, 8th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=8 & CTI_num!=12, cluster(id) 
scalar b194 = _b[inter]
scalar p194 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th, 9th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=9 & CTI_num!=10, cluster(id) 
scalar b195 = _b[inter]
scalar p195 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=6&9&10: Coef=" %6.3f b195 "   p=" %6.3f p195

* Without the 6th, 9th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=9 & CTI_num!=11, cluster(id) 
scalar b196 = _b[inter]
scalar p196 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th, 9th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=9 & CTI_num!=12, cluster(id) 
scalar b197 = _b[inter]
scalar p197 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th, 10th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=10 & CTI_num!=11, cluster(id) 
scalar b198 = _b[inter]
scalar p198 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=6&10&11: Coef=" %6.3f b198 "   p=" %6.3f p198

* Without the 6th, 10th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=10 & CTI_num!=12, cluster(id) 
scalar b199 = _b[inter]
scalar p199 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 6th, 11th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=6 & CTI_num!=11 & CTI_num!=12, cluster(id) 
scalar b200 = _b[inter]
scalar p200 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=6&11&12: Coef=" %6.3f b200 "   p=" %6.3f p200

* Without the 7th, 8th, and 9th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=8 & CTI_num!=9, cluster(id) 
scalar b201 = _b[inter]
scalar p201 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=7&8&9: Coef=" %6.3f b201 "   p=" %6.3f p201

* Without the 7th, 8th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=8 & CTI_num!=10, cluster(id) 
scalar b202 = _b[inter]
scalar p202 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 7th, 8th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=8 & CTI_num!=11, cluster(id) 
scalar b203 = _b[inter]
scalar p203 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 7th, 8th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=8 & CTI_num!=12, cluster(id) 
scalar b204 = _b[inter]
scalar p204 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 7th, 9th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=9 & CTI_num!=10, cluster(id) 
scalar b205 = _b[inter]
scalar p205 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=7&9&10: Coef=" %6.3f b205 "   p=" %6.3f p205

* Without the 7th, 9th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=9 & CTI_num!=11, cluster(id) 
scalar b206 = _b[inter]
scalar p206 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 7th, 9th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=9 & CTI_num!=12, cluster(id) 
scalar b207 = _b[inter]
scalar p207 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 7th, 10th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=10 & CTI_num!=11, cluster(id) 
scalar b208 = _b[inter]
scalar p208 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=7&10&11: Coef=" %6.3f b208 "   p=" %6.3f p208

* Without the 7th, 10th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=10 & CTI_num!=12, cluster(id) 
scalar b209 = _b[inter]
scalar p209 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 7th, 11th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=7 & CTI_num!=11 & CTI_num!=12, cluster(id) 
scalar b210 = _b[inter]
scalar p210 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=7&11&12: Coef=" %6.3f b210 "   p=" %6.3f p210

* Without the 8th, 9th, and 10th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=8 & CTI_num!=9 & CTI_num!=10, cluster(id) 
scalar b211 = _b[inter]
scalar p211 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=8&9&10: Coef=" %6.3f b211 "   p=" %6.3f p211

* Without the 8th, 9th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=8 & CTI_num!=9 & CTI_num!=11, cluster(id) 
scalar b212 = _b[inter]
scalar p212 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 8th, 9th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=8 & CTI_num!=9 & CTI_num!=12, cluster(id) 
scalar b213 = _b[inter]
scalar p213 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 8th, 10th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=8 & CTI_num!=10 & CTI_num!=11, cluster(id) 
scalar b214 = _b[inter]
scalar p214 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=8&10&11: Coef=" %6.3f b214 "   p=" %6.3f p214

* Without the 8th, 10th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=8 & CTI_num!=10 & CTI_num!=12, cluster(id) 
scalar b215 = _b[inter]
scalar p215 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 8th, 11th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=8 & CTI_num!=11 & CTI_num!=12, cluster(id) 
scalar b216 = _b[inter]
scalar p216 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=8&11&12: Coef=" %6.3f b216 "   p=" %6.3f p216

* Without the 9th, 10th, and 11th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=9 & CTI_num!=10 & CTI_num!=11, cluster(id) 
scalar b217 = _b[inter]
scalar p217 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=9&10&11: Coef=" %6.3f b217 "   p=" %6.3f p217

* Without the 9th, 10th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=9 & CTI_num!=10 & CTI_num!=12, cluster(id) 
scalar b218 = _b[inter]
scalar p218 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))

* Without the 9th, 11th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=9 & CTI_num!=11 & CTI_num!=12, cluster(id) 
scalar b219 = _b[inter]
scalar p219 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=9&11&12: Coef=" %6.3f b219 "   p=" %6.3f p219

* Without the 10th, 11th, and 12th "partisan" CTs
reg belief_CT CT_scale i.congenial_bloc inter i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 & CTI_num!=10 & CTI_num!=11 & CTI_num!=12, cluster(id) 
scalar b220 = _b[inter]
scalar p220 = 2 * ttail(e(df_r), abs(_b[inter]/_se[inter]))
di "CTI_num!=10&11&12: Coef=" %6.3f b220 "   p=" %6.3f p220

gen b_inter=.
gen p_inter=.
replace b_inter=b1 in 1
replace b_inter=b2 in 2
replace b_inter=b3 in 3
replace b_inter=b4 in 4
replace b_inter=b5 in 5
replace b_inter=b6 in 6
replace b_inter=b7 in 7
replace b_inter=b8 in 8
replace b_inter=b9 in 9
replace b_inter=b10 in 10
replace b_inter=b11 in 11
replace b_inter=b12 in 12
replace b_inter=b13 in 13
replace b_inter=b14 in 14
replace b_inter=b15 in 15
replace b_inter=b16 in 16
replace b_inter=b17 in 17
replace b_inter=b18 in 18
replace b_inter=b19 in 19
replace b_inter=b20 in 20
replace b_inter=b21 in 21
replace b_inter=b22 in 22
replace b_inter=b23 in 23
replace b_inter=b24 in 24
replace b_inter=b25 in 25
replace b_inter=b26 in 26
replace b_inter=b27 in 27
replace b_inter=b28 in 28
replace b_inter=b29 in 29
replace b_inter=b30 in 30
replace b_inter=b31 in 31
replace b_inter=b32 in 32
replace b_inter=b33 in 33
replace b_inter=b34 in 34
replace b_inter=b35 in 35
replace b_inter=b36 in 36
replace b_inter=b37 in 37
replace b_inter=b38 in 38
replace b_inter=b39 in 39
replace b_inter=b40 in 40
replace b_inter=b41 in 41
replace b_inter=b42 in 42
replace b_inter=b43 in 43
replace b_inter=b44 in 44
replace b_inter=b45 in 45
replace b_inter=b46 in 46
replace b_inter=b47 in 47
replace b_inter=b48 in 48
replace b_inter=b49 in 49
replace b_inter=b50 in 50
replace b_inter=b51 in 51
replace b_inter=b52 in 52
replace b_inter=b53 in 53
replace b_inter=b54 in 54
replace b_inter=b55 in 55
replace b_inter=b56 in 56
replace b_inter=b57 in 57
replace b_inter=b58 in 58
replace b_inter=b59 in 59
replace b_inter=b60 in 60
replace b_inter=b61 in 61
replace b_inter=b62 in 62
replace b_inter=b63 in 63
replace b_inter=b64 in 64
replace b_inter=b65 in 65
replace b_inter=b66 in 66
replace b_inter=b67 in 67
replace b_inter=b68 in 68
replace b_inter=b69 in 69
replace b_inter=b70 in 70
replace b_inter=b71 in 71
replace b_inter=b72 in 72
replace b_inter=b73 in 73
replace b_inter=b74 in 74
replace b_inter=b75 in 75
replace b_inter=b76 in 76
replace b_inter=b77 in 77
replace b_inter=b78 in 78
replace b_inter=b79 in 79
replace b_inter=b80 in 80
replace b_inter=b81 in 81
replace b_inter=b82 in 82
replace b_inter=b83 in 83
replace b_inter=b84 in 84
replace b_inter=b85 in 85
replace b_inter=b86 in 86
replace b_inter=b87 in 87
replace b_inter=b88 in 88
replace b_inter=b89 in 89
replace b_inter=b90 in 90
replace b_inter=b91 in 91
replace b_inter=b92 in 92
replace b_inter=b93 in 93
replace b_inter=b94 in 94
replace b_inter=b95 in 95
replace b_inter=b96 in 96
replace b_inter=b97 in 97
replace b_inter=b98 in 98
replace b_inter=b99 in 99
replace b_inter=b100 in 100
replace b_inter=b101 in 101
replace b_inter=b102 in 102
replace b_inter=b103 in 103
replace b_inter=b104 in 104
replace b_inter=b105 in 105
replace b_inter=b106 in 106
replace b_inter=b107 in 107
replace b_inter=b108 in 108
replace b_inter=b109 in 109
replace b_inter=b110 in 110
replace b_inter=b111 in 111
replace b_inter=b112 in 112
replace b_inter=b113 in 113
replace b_inter=b114 in 114
replace b_inter=b115 in 115
replace b_inter=b116 in 116
replace b_inter=b117 in 117
replace b_inter=b118 in 118
replace b_inter=b119 in 119
replace b_inter=b120 in 120
replace b_inter=b121 in 121
replace b_inter=b122 in 122
replace b_inter=b123 in 123
replace b_inter=b124 in 124
replace b_inter=b125 in 125
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replace b_inter=b151 in 151
replace b_inter=b152 in 152
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replace b_inter=b189 in 189
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replace b_inter=b199 in 199
replace b_inter=b200 in 200
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replace b_inter=b216 in 216
replace b_inter=b217 in 217
replace b_inter=b218 in 218
replace b_inter=b219 in 219
replace b_inter=b220 in 220

replace p_inter=p1 in 1
replace p_inter=p2 in 2
replace p_inter=p3 in 3
replace p_inter=p4 in 4
replace p_inter=p5 in 5
replace p_inter=p6 in 6
replace p_inter=p7 in 7
replace p_inter=p8 in 8
replace p_inter=p9 in 9
replace p_inter=p10 in 10
replace p_inter=p11 in 11
replace p_inter=p12 in 12
replace p_inter=p13 in 13
replace p_inter=p14 in 14
replace p_inter=p15 in 15
replace p_inter=p16 in 16
replace p_inter=p17 in 17
replace p_inter=p18 in 18
replace p_inter=p19 in 19
replace p_inter=p20 in 20
replace p_inter=p21 in 21
replace p_inter=p22 in 22
replace p_inter=p23 in 23
replace p_inter=p24 in 24
replace p_inter=p25 in 25
replace p_inter=p26 in 26
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replace p_inter=p28 in 28
replace p_inter=p29 in 29
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replace p_inter=p35 in 35
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replace p_inter=p115 in 115
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replace p_inter=p117 in 117
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replace p_inter=p120 in 120
replace p_inter=p121 in 121
replace p_inter=p122 in 122
replace p_inter=p123 in 123
replace p_inter=p124 in 124
replace p_inter=p125 in 125
replace p_inter=p126 in 126
replace p_inter=p127 in 127
replace p_inter=p128 in 128
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replace p_inter=p130 in 130
replace p_inter=p131 in 131
replace p_inter=p132 in 132
replace p_inter=p133 in 133
replace p_inter=p134 in 134
replace p_inter=p135 in 135
replace p_inter=p136 in 136
replace p_inter=p137 in 137
replace p_inter=p138 in 138
replace p_inter=p139 in 139
replace p_inter=p140 in 140
replace p_inter=p141 in 141
replace p_inter=p142 in 142
replace p_inter=p143 in 143
replace p_inter=p144 in 144
replace p_inter=p145 in 145
replace p_inter=p146 in 146
replace p_inter=p147 in 147
replace p_inter=p148 in 148
replace p_inter=p149 in 149
replace p_inter=p150 in 150
replace p_inter=p151 in 151
replace p_inter=p152 in 152
replace p_inter=p153 in 153
replace p_inter=p154 in 154
replace p_inter=p155 in 155
replace p_inter=p156 in 156
replace p_inter=p157 in 157
replace p_inter=p158 in 158
replace p_inter=p159 in 159
replace p_inter=p160 in 160
replace p_inter=p161 in 161
replace p_inter=p162 in 162
replace p_inter=p163 in 163
replace p_inter=p164 in 164
replace p_inter=p165 in 165
replace p_inter=p166 in 166
replace p_inter=p167 in 167
replace p_inter=p168 in 168
replace p_inter=p169 in 169
replace p_inter=p170 in 170
replace p_inter=p171 in 171
replace p_inter=p172 in 172
replace p_inter=p173 in 173
replace p_inter=p174 in 174
replace p_inter=p175 in 175
replace p_inter=p176 in 176
replace p_inter=p177 in 177
replace p_inter=p178 in 178
replace p_inter=p179 in 179
replace p_inter=p180 in 180
replace p_inter=p181 in 181
replace p_inter=p182 in 182
replace p_inter=p183 in 183
replace p_inter=p184 in 184
replace p_inter=p185 in 185
replace p_inter=p186 in 186
replace p_inter=p187 in 187
replace p_inter=p188 in 188
replace p_inter=p189 in 189
replace p_inter=p190 in 190
replace p_inter=p191 in 191
replace p_inter=p192 in 192
replace p_inter=p193 in 193
replace p_inter=p194 in 194
replace p_inter=p195 in 195
replace p_inter=p196 in 196
replace p_inter=p197 in 197
replace p_inter=p198 in 198
replace p_inter=p199 in 199
replace p_inter=p200 in 200
replace p_inter=p201 in 201
replace p_inter=p202 in 202
replace p_inter=p203 in 203
replace p_inter=p204 in 204
replace p_inter=p205 in 205
replace p_inter=p206 in 206
replace p_inter=p207 in 207
replace p_inter=p208 in 208
replace p_inter=p209 in 209
replace p_inter=p210 in 210
replace p_inter=p211 in 211
replace p_inter=p212 in 212
replace p_inter=p213 in 213
replace p_inter=p214 in 214
replace p_inter=p215 in 215
replace p_inter=p216 in 216
replace p_inter=p217 in 217
replace p_inter=p218 in 218
replace p_inter=p219 in 219
replace p_inter=p220 in 220


* A histogram of the 'jackknife' coefficients
twoway hist b_inter, bin(8) xscale(range(0 .25)) ///
xlabel(0(.05).25, labsize(medium)) scheme(s2mono) legend(off) ///
ylabel(0(5)20, angle(0) labsize(medium)) ///
graphregion(fcolor(white)) plotregion(margin(5 5 2 2)) /// 
xtitle("The interaction coefficient", size(medium)) ///
title(" ", size(medium))
/* || kdensity b_inter */
/* title("Study 1 estimates: Each time dropping three conspiracy theories"*/

sum b_inter p_inter
drop b_inter p_inter


*******************************************************************************
** Changing the "classification" of diff. non-partisan CTs - Online Appendix **
*******************************************************************************

*** "Congenial bloc" dummy - 2nd version (including non-partisan CTs with >10 pp partisan gaps) **
fre CTI_num
gen congenial_bloc2=congenial_bloc
replace congenial_bloc2=1 if CTI_num==23 & rep==1
replace congenial_bloc2=0 if CTI_num==23 & rep==0
replace congenial_bloc2=1 if CTI_num==24 & rep==1
replace congenial_bloc2=0 if CTI_num==24 & rep==0
replace congenial_bloc2=1 if CTI_num==28 & rep==1
replace congenial_bloc2=0 if CTI_num==28 & rep==0
replace congenial_bloc2=1 if CTI_num==29 & rep==1
replace congenial_bloc2=0 if CTI_num==29 & rep==0
replace congenial_bloc2=1 if CTI_num==36 & rep==0
replace congenial_bloc2=0 if CTI_num==36 & rep==1

label var congenial_bloc2 "Congenial bloc - 2nd version"
tab congenial_bloc2
tab CTI_num congenial_bloc2 if rep==1
tab CTI_num congenial_bloc2 if rep==0

** Table F1 - Partisan CTs **
* Model 1 - with the five additional "now-partisan" CTs
reg belief_CT c.CT_scale##i.congenial_bloc2 i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 | CTI_num==23 | CTI_num==24 | CTI_num==28 | CTI_num==29 | CTI_num==36, cluster(id)
outreg2 using Table_F1.doc, replace se dec(3) alpha (.001, .01, .05) ///
symbol (***, **, *) keep(c.CT_scale##i.congenial_bloc2) ctitle (" ") ///
addtext(CT Fixed-effects, "YES", Individual-level controls, "YES") ///
title("Table F1. Study 1: Predicting belief in partisan conspiracy theories, with five additional 'now-partisan' conspiracy theories")

* Model 2 - The original Model 1 in Table 1 (for a comparison)
reg belief_CT c.CT_scale##i.congenial_bloc i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num, cluster(id)
outreg2 using Table_F1.doc, append se dec(3) alpha (.001, .01, .05) ///
symbol (***, **, *) keep(c.CT_scale##i.congenial_bloc) ///
ctitle ("The original Model 1 in Table 1") ///
addtext(CT Fixed-effects, "YES", Individual-level controls, "YES") ///
title("Table F1. Study 1: Predicting belief in partisan conspiracy theories, with five additional 'now-partisan' conspiracy theories")


** Table F2 - Non-partisan CTs **
* Model 1 - excluding the five "now-partisan" CTs
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num>12 & (CTI_num!=23 & CTI_num!=24 & CTI_num!=28 & CTI_num!=29 & CTI_num!=36), cluster(id)
outreg2 using Table_F2.doc, replace se dec(3) alpha (.001, .01, .05) ///
symbol (***, **, *) keep(c.CT_scale##i.rep) ///
ctitle (" ") ///
addtext(CT Fixed-effects, "YES", Individual-level controls, "YES") ///
title ("Table F2. Study 1: Predicting belief in non-partisan conspiracy theories, excluding five 'now-partisan' conspiracy theories")

* Model 2 - The original Model 1 in Table 3 (for a comparison)
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num>12, cluster(id)
outreg2 using Table_F2.doc, append se dec(3) alpha (.001, .01, .05) ///
symbol (***, **, *) keep(c.CT_scale##i.rep) ///
ctitle ("The original Model 1 in Table 3") ///
addtext(CT Fixed-effects, "YES", Individual-level controls, "YES") ///
title ("Table F2. Study 1: Predicting belief in non-partisan conspiracy theories, excluding five 'now-partisan' conspiracy theories")


*** "Congenial bloc" dummy - 3rd version (including non-partisan CTs with 5-10 pp partisan gaps) **
fre CTI_num
gen congenial_bloc3=congenial_bloc2
replace congenial_bloc3=1 if CTI_num==19 & rep==1
replace congenial_bloc3=0 if CTI_num==19 & rep==0
replace congenial_bloc3=1 if CTI_num==20 & rep==1
replace congenial_bloc3=0 if CTI_num==20 & rep==0
replace congenial_bloc3=1 if CTI_num==16 & rep==0
replace congenial_bloc3=0 if CTI_num==16 & rep==1
replace congenial_bloc3=1 if CTI_num==22 & rep==0
replace congenial_bloc3=0 if CTI_num==22 & rep==1
replace congenial_bloc3=1 if CTI_num==35 & rep==0
replace congenial_bloc3=0 if CTI_num==35 & rep==1

label var congenial_bloc3 "Congenial bloc - 3rd version"
tab congenial_bloc3
tab CTI_num congenial_bloc3 if rep==1
tab CTI_num congenial_bloc3 if rep==0

* Partisan CTs
reg belief_CT c.CT_scale##i.congenial_bloc3 i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num, cluster(id)
reg belief_CT c.CT_scale##i.congenial_bloc3 i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num<13 | CTI_num==16 | CTI_num==19 | CTI_num==20 | CTI_num==22 | CTI_num==35 | CTI_num==23 | CTI_num==24 | CTI_num==28 | CTI_num==29 | CTI_num==36 | CTI_num==38, cluster(id)

* Non-partisan CTs
reg belief_CT c.CT_scale##i.rep i.gender i.age_group i.white i.college_educ income i.ideo_group i.CTI_num if CTI_num>12 & (CTI_num!=23 & CTI_num!=24 & CTI_num!=28 & CTI_num!=29 & CTI_num!=36 & CTI_num!=16 & CTI_num!=19 & CTI_num!=20 & CTI_num!=22 & CTI_num!=35  & CTI_num!=38), cluster(id)



****************************************************
** Descriptive statistics for the Online Appendix **
****************************************************

* Changing directory
**<<<ADD DIRECTORY>>>

* Loading the dataset data (.dta)
use "Study 1 data.dta", clear


* Gender
gen gender=1 if female==1
replace gender=0 if female==0
label define gender_lab 0 "Male" 1 "Female"
label values gender gender_lab
tab gender female
label var gender "Gender (female=1)"
fre gender

* Age 
sum age
recode age (18/29=1) (30/49=2) (50/69=3) (70/99=4), gen (age_group)
label define ageg2_lab 1 "18-29" 2 "30-49" 3 "50-69" 4 "70+"
label value age_group ageg2_lab
tab age age_group
tab age_group

* College education
recode edu (1/2=0)(3/6=1), gen(college_educ)
label define coll_lab 0 "No college education" 1 "College education"
label values college_educ coll_lab
tab edu college_educ
label var college_educ "College education"
fre college_educ

* White respondent
rename white white2
gen white=1 if white2==1
replace white=0 if white2==0
label define white_lab 0 "Non-White" 1 "White"
label values white white_lab
tab white2 white
label var white "White respondent"
fre white

* PID
rename rep rep2
gen rep=1 if rep2==1
replace rep=0 if rep2==0
label define rep_lab 0 "Democrat" 1 "Republican"
label values rep rep_lab
tab rep2 rep, miss
label var rep "Republican respondent"
fre rep
